Online fashion shopping is booming, yet high return rates persist as actual size and fit do not meet shoppers’ expectations. Virtual Try-On (VTON), rendering garments on individuals’ body images, promises to reduce such issues. To understand VTON’s impact on shopping behaviors and experiences, we conducted user study with 24 participants where they were asked to explore and purchase clothing online and then shared their thoughts on satisfaction, similarity, and return intentions after wearing items. Results show that VTON reduced exploration time and product detail views, while enabling clearer expectations of fit before delivery. Importantly, participants often used VTON for final verification, while some sought to discover new styles, suggesting VTON should provide adaptive support according to users’ tendencies. Participants also emphasized fit accuracy, highlighting the need for technical improvements and reliability cues such as confidence scores. Building on these findings, we suggest design implications for integrating VTON into e-commerce.

Abstract Single‐cell RNA sequencing (scRNA‐seq) has gained prominence as a valuable technique for examining cellular gene expression patterns at the individual cell level. In the analysis of scRNA‐seq datasets, it is common practice to visualise a subset of principal components (PCs), obtained via principal component analysis (PCA), using dimensionality reduction techniques such as t‐stochastic neighbour embedding (t‐SNE). Determining the number of PCs (i.e. dimensionality) is a critical step that influences the outcome of single‐cell analysis, and this process typically requires a labour‐intensive manual assessment involving the inspection of numerous projection plots. To address this challenge, we present a visualisation system that assists analysts in efficiently determining the optimal dimensionality of scRNA‐seq data. The proposed system employs two hull heatmaps, a cell type heatmap and a cluster heatmap, which offer comprehensive representations of target cells of multiple cell types across various dimensionalities through the utilisation of a convex hull‐embedded colour map. The cell type heatmap shows overlaps between cell types, and the cluster heatmap compares cell clustering results. The proposed hull heatmaps effectively alleviate the labourious task of manually evaluating hundreds of projection plots for searching for the optimal dimensionality. Additionally, our system offers interactive visualisation of gene expression levels and an intuitive lasso selection tool, thereby enabling analysts to progressively refine the convex hulls on the hull heatmaps. We validated the usefulness of the proposed system through two quantitative evaluations and three case studies.

To improve the accessibility of visual figures, auto-generation of text description of individual images has been studied. However, it cannot be directly applied to comics as the descriptions can be redundant as similar scenes appear in a row. To address this issue, we propose generating the descriptions per group of related images and demonstrate how an dense captioning technique for videos can be utilized for this purpose and ways to improve its performance. To assess the effectiveness of our approach and to identify factors affecting the quality of text descriptions of comics, we conducted a preliminary study with 3 sighted evaluators and a main user study with 12 participants with visual impairments. The results show that text descriptions generated per group of images are perceived to be better than those generated per image in terms of accuracy, clarity, understandability, length, informativeness and preference for sighted groups, when annotator is human. In the same conditions, when the annotator is AI, it exhibited better performance in terms of length. Also, people with visual impairments prefer group descriptions because of conciseness, smooth connectivity of sentences, and non-repetitive features. Based on the findings, we provide design recommendations for generating accessible comic descriptions at a scale for blind users.

Single-cell RNA sequencing (scRNA-seq) is becoming popular in studying the gene expression of cells at the single-cell level. ScRNA-seq enables analysts to characterize cell types, thereby providing a better understanding of dynamic biological processes. In scRNA-seq data analysis, principal component analysis (PCA) is commonly used to reduce at least thousands of dimensions in the raw data to a manageable size so that analysts can visualize and cluster cells to identify different cell types. The conventional process to determine the optimal dimensionality includes a laborious manual review of hundreds of different projection plots. To address this problem, we introduce a dimensionality explorer for single-cell analysis, which is a visualization system that helps analysts to effectively determine the optimal dimensionality of scRNA-seq data. It employs a hull heatmap, which provides a holistic view of overlaps among multiple cell types across various dimensionalities using a convex hull-embedded color map. The hull heatmap effectively reduces the burden of manually reviewing hundreds of projection plots to determine the optimal dimensionality. Our system also provides interactive gene expression level visualization and intuitive lasso selection, thereby allowing analysts to progressively refine the convex hulls of the hull heatmap. We demonstrate the usefulness of the proposed system through a user study and three case studies conducted by domain experts.

The accurate prediction of cancer drug sensitivity according to the multiomics profiles of individual patients is crucial for precision cancer medicine. However, the development of prediction models has been challenged by the complex crosstalk of input features and the resistance-dominant drug response information contained in public databases. In this study, we propose a novel multidrug response prediction framework, response-aware multitask prediction (RAMP), via a Bayesian neural network and restrict it by soft-supervised contrastive regularization. To utilize network embedding vectors as representation learning features for heterogeneous networks, we harness response-aware negative sampling, which applies cell line-drug response information to the training of network embeddings. RAMP overcomes the prediction accuracy limitation induced by the imbalance of trained response data based on the comprehensive selection and utilization of drug response features. When trained on the Genomics of Drug Sensitivity in Cancer dataset, RAMP achieved an area under the receiver operating characteristic curve > 89%, an area under the precision-recall curve > 59% and an $\textrm{F}_1$ score > 52% and outperformed previously developed methods on both balanced and imbalanced datasets. Furthermore, RAMP predicted many missing drug responses that were not included in the public databases. Our results showed that RAMP will be suitable for the high-throughput prediction of cancer drug sensitivity and will be useful for guiding cancer drug selection processes. The Python implementation for RAMP is available at https://github.com/hvcl/RAMP.

Whole-exome sequencing (WES) has become a standard method for detecting genetic variants in human diseases. Although the primary use of WES data has been the identification of single nucleotide variations and indels, these data also offer a possibility of detecting copy number variations (CNVs) at high resolution. However, WES data have uneven read coverage along the genome owing to the target capture step, and the development of a robust WES-based CNV tool is challenging. Here, we evaluate six WES somatic CNV detection tools: ADTEx, CONTRA, Control-FREEC, EXCAVATOR, ExomeCNV and Varscan2. Using WES data from 50 kidney chromophobe, 50 bladder urothelial carcinoma, and 50 stomach adenocarcinoma patients from The Cancer Genome Atlas, we compared the CNV calls from the six tools with a reference CNV set that was identified by both single nucleotide polymorphism array 6.0 and whole-genome sequencing data. We found that these algorithms gave highly variable results: visual inspection reveals significant differences between the WES-based segmentation profiles and the reference profile, as well as among the WES-based profiles. Using a 50% overlap criterion, 13-77% of WES CNV calls were covered by CNVs from the reference set, up to 21% of the copy gains were called as losses or vice versa, and dramatic differences in CNV sizes and CNV numbers were observed. Overall, ADTEx and EXCAVATOR had the best performance with relatively high precision and sensitivity. We suggest that the current algorithms for somatic CNV detection from WES data are limited in their performance and that more robust algorithms are needed.

A precise and implantable micromanipulator is presented for automatically advancing electrodes during single unit recordings in freely-behaving animals. The modular design and enhanced clamping mechanism with simple mechanical components are designed to provide reliable linear motion using a piezo motor with a stroke of 3 mm. To be specific, a closed loop control system, based on the position feedback from a magnetoresistive (MR) sensor, was implemented to overcome the non-linear characteristics of the piezo motor and to locate electrodes precisely at the targeted position with the accuracy of 1 μm, even under load. The weight of the micromanipulator is only 0.84 g when it is fully assembled with the MR sensor, PCBs, and connectors. In addition, a protective cover is employed to prevent breakage during semi-chronic recording. The positioning performance of the micromanipulator was tested at various loading conditions using various control methods. Finally, the activities of a single unit were isolated successfully using small step adjustments, such as 1 to 5 μm, in freely-moving mice.

UNLABELLED: BIPA is a database for protein-nucleic acid interactions in 3D structures. The database provides various physicochemical features of protein-nucleic acid interface such as size, shape, residue propensity, secondary structure composition and intermolecular interactions. The database also contains multiple structural alignments of nucleic acid-binding protein families with annotations of local environments in order to allow definition of features that influence acceptability of mutations at a particular position in a protein family. A web interface has been designed to present the results of these analyses and facilitate navigation of protein-nucleic acid interfaces. AVAILABILITY: http://www-cryst.bioc.cam.ac.uk/bipa SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Amino acid residues are under various kinds of local environmental restraints, which influence substitution patterns. Ulla,(1) a program for calculating environment-specific substitution tables, reads protein sequence alignments and local environment annotations. The program produces a substitution table for every possible combination of environment features. Sparse data is handled using an entropy-based smoothing procedure to estimate robust substitution probabilities. AVAILABILITY: The Ruby source code is available under a Creative Commons Attribution-Noncommercial License along with additional documentation from http://www-cryst.bioc.cam.ac.uk/ulla. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Many genomes have been completely sequenced. However, detecting and analyzing their protein-protein interactions by experimental methods such as co-immunoprecipitation, tandem affinity purification and Y2H is not as fast as genome sequencing. Therefore, a computational prediction method based on the known protein structural interactions will be useful to analyze large-scale protein-protein interaction rules within and among complete genomes. RESULTS: We confirmed that all the predicted protein family interactomes (the full set of protein family interactions within a proteome) of 146 species are scale-free networks, and they share a small core network comprising 36 protein families related to indispensable cellular functions. We found two fundamental differences among prokaryotic and eukaryotic interactomes: (1) eukarya had significantly more hub families than archaea and bacteria and (2) certain special hub families determined the topology of the eukaryotic interactomes. Our comparative analysis suggests that a very small number of expansive protein families led to the evolution of interactomes and seemed to have played a key role in species diversification. SUPPLEMENTARY INFORMATION: http://interactomics.org.